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Detection of Brain Tumor in MRI Images Using Watershed and Threshold-Based Segmentation

Ghinwa M. Tarhini and Reda Shbib
Department of Computer Science, Lebanese International University, Saida, Lebanon
Abstract—Brain Cells control all body organs including the critical ones where any disorder of these cells will directly cause other organs to malfunction and thus threatens human’s life. That is why the brain is considered the most crucial organ in the human body. Disorder of brain cells, appear as inflamed cells in the brain known as a tumor. The early detection of these tumorous cells will increase the possibility of curing disease in a short time. Magnetic Resonance Imaging (MRI) is now used for detection of brain tumor. Image processing and segmentation of the MRI images is now an emerging research field where several techniques have been developed for MRI segmentation and tumor detection like Fuzzy C-Means (FCM) and Support Vector Machine (SVM). In this paper, an efficient algorithm based on threshold segmentation technique, followed by some morphological operations is proposed. The quality of the MRI scanned image is enhanced at first, then threshold segmentation is applied to divide the pixels into different classes, then to detect the tumor part of the image with the highest intensity, morphological operators are applied. 

Index Terms—brain tumor, image processing, MRI, threshold segmentation

Cite: Ghinwa M. Tarhini and Reda Shbib, "Detection of Brain Tumor in MRI Images Using Watershed and Threshold-Based Segmentation," International Journal of Signal Processing Systems, Vol. 8, No. 1, pp. 19-25, March 2020. doi: 10.18178/ijsps.8.1.19-25

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